中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

10 records (took 0.015 seconds)

    THIRD PARTY BASED PIRATED COPY TRACING
    3.
    发明公开
    THIRD PARTY BASED PIRATED COPY TRACING 审中-公开

    公开(公告)号：US20230205851A1

    公开(公告)日：2023-06-29

    申请号：US17926997

    申请日：2021-05-10

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Xian Zhang ,  Xiaobing Guo ,  Yang Chen ,  Shuo Chen ,  Zhongxin Guo ,  Qiufeng Yin ,  Mao Yang ,  Lidong Zhou

    IPC: G06F21/10

    CPC classification number: G06F21/105

    Abstract: According to implementations of the subject matter described herein, a solution is provided for pirated copy tracing based on a third party. In the solution, a report on a pirated copy of a digital content is received from a third party, wherein the report comprises first secret information for characterizing a first identification, time information and tracing information of the pirated copy. Subsequently, a request for verifying the report is received to determine whether the report is valid. When the report is determined as valid, a licensee associated with the report is marked as a first status to indicate that the pirated copy might be leaked by the licensee. Therefore, the pirated copy may be effectively traced based on third parties. The tracing information in the report can be hidden, and other third parties can therefore be prevented from using the tracing information for duplicate reports.

    RESOURCE USAGE PREDICTION FOR DEEP LEARNING MODEL
    5.
    发明申请
    RESOURCE USAGE PREDICTION FOR DEEP LEARNING MODEL 有权

    公开(公告)号：US20230035451A1

    公开(公告)日：2023-02-02

    申请号：US17783247

    申请日：2020-12-09

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Yanjie GAO ,  Haoxiang Lin ,  Yuci Liu ,  Mao Yang

    IPC: G06N3/08

    Abstract: According to implementations of the subject matter described herein, there is provided a solution for predicting the resource usage of the deep learning model. In this solution, information about a deep learning model is obtained, the information comprising first information for describing the deep learning model and second information about an operating environment of a job associated with the deep learning model. The static resource usage of the job is determined based on the first information and a strategy of the job during runtime in the operating environment is determined. Afterwards, resource usage of the job during runtime in the operating environment is predicted based on the strategy and the static resource usage. With this solution, the usage of various resources of the deep learning model, such as computation power consumption, memory consumption, execution time, and the like, under a specific runtime strategy can be accurately predicted.

    DYNAMIC ALLOCATION OF COMPUTING RESOURCES
    6.
    发明申请
    DYNAMIC ALLOCATION OF COMPUTING RESOURCES 有权

    公开(公告)号：US20220229701A1

    公开(公告)日：2022-07-21

    申请号：US17609700

    申请日：2020-05-04

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Quanlu Zhang ,  Lidong Zhou ,  Mao Yang ,  Fan Yang ,  Hanyu Zhao ,  Zhenhua Han

    IPC: G06F9/50

    Abstract: According to implementations of the subject matter, a solution of dynamic management of computing resource is provided. In the solution, a first request for using a target number of computing resource in a set of computing resources is received, wherein at least one free computing resource of the set of computing resources is organized into at least one free resource group. When it is determined that a free matching resource group is absent from the first resource group and a free redundant resource group is present in at least one free resource group, the target number of computing resources are allocated for the first request by splitting the free redundant resource group, wherein the number of resources in the free redundant resource group is greater than the target number. Therefore, the dynamic allocation of computing resources is enabled.

    Visual Programming for Deep Learning
    7.
    发明申请
    Visual Programming for Deep Learning 有权

    公开(公告)号：US20240370237A1

    公开(公告)日：2024-11-07

    申请号：US18774696

    申请日：2024-07-16

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Haoxiang Lin ,  Mao Yang ,  Shuguang Liu ,  Cheng Chen

    IPC: G06F8/34 ,  G06F3/0486 ,  G06N3/082

    Abstract: Implementations of the present disclosure relate to visual programming for deep learning. A computer-implemented method comprises presenting a visual representation of an artificial neural network, the visual representation comprising graphical elements representing layers of the artificial neural network; in response to receiving a drag-and-drop operation on the graphical elements, modifying an intermediate representation of the artificial neural network, wherein the intermediate representation is independent of a deep learning framework and the drag-and-drop operation is configured to modify connections between the graphical elements; and modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network for a target deep learning framework.

    Visual programming for deep learning
    8.
    发明授权
    Visual programming for deep learning 有权

    公开(公告)号：US12079600B2

    公开(公告)日：2024-09-03

    申请号：US17615080

    申请日：2020-05-06

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Haoxiang Lin ,  Mao Yang ,  Shuguang Liu ,  Cheng Chen

    IPC: G06F8/34 ,  G06F3/0486 ,  G06N3/082

    CPC classification number: G06F8/34 ,  G06F3/0486 ,  G06N3/082

    Abstract: Implementations of the present disclosure relate to visual programming for deep learning. A computer-implemented method comprises presenting a visual representation of an artificial neural network, the visual representation comprising graphical elements representing layers of the artificial neural network; in response to receiving a drag-and-drop operation on the graphical elements, modifying an intermediate representation of the artificial neural network, wherein the intermediate representation is independent of a deep learning framework and the drag-and-drop operation is configured to modify connections between the graphical elements; and modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network for a target deep learning framework.

    Multi-layer search-engine index
    9.
    发明授权
    Multi-layer search-engine index 有权

    公开(公告)号：US09959347B2

    公开(公告)日：2018-05-01

    申请号：US14623022

    申请日：2015-02-16

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： Hui Shen ,  Mao Yang ,  Lintao Zhang ,  Zhenyu Zhao ,  Xiao Wu ,  Ying Yan ,  Xiaosong Yang ,  Chad Walters ,  Choong Soon Chang

    IPC: G06F17/30

    CPC classification number: G06F17/30864 ,  G06F17/30312

    Abstract: Subject matter described herein includes a multi-layer search-engine index. Accordingly, the search-engine index is divided into multiple indexes, each of which includes a respective set of information used to serve (i.e., respond to) a query. One index includes a term index, which organizes a set of terms that are found among a collection of documents. Another index includes a document index, which organizes a set of documents that are searchable. A computing device is used to serve the search-engine index (i.e., to analyze the index when identifying documents relevant to a search query). For example, a solid-state device might be used to serve the multi-layer search-engine index.

    Visual Programming for Deep Learning
    10.
    发明申请
    Visual Programming for Deep Learning 有权

    公开(公告)号：US20220222049A1

    公开(公告)日：2022-07-14

    申请号：US17615080

    申请日：2020-05-06

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Haoxiang Lin ,  Mao Yang ,  Shuguang Liu ,  Cheng Chen

    IPC: G06F8/34 ,  G06N3/08 ,  G06F3/0486

    Abstract: Implementations of the present disclosure relate to visual programming for deep learning. A computer-implemented method comprises presenting a visual representation of an artificial neural network, the visual representation comprising graphical elements representing layers of the artificial neural network; in response to receiving a drag-and-drop operation on the graphical elements, modifying an intermediate representation of the artificial neural network, wherein the intermediate representation is independent of a deep learning framework and the drag-and-drop operation is configured to modify connections between the graphical elements; and modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network for a target deep learning framework.

Patent Agency Ranking